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Purpose The purpose of this paper is to explore public perceptions and key issues related to vasectomy in South Korea. In particular, this paper seeks to analyze how vasectomy is discussed on question-and-answer (Q&A) social platforms including NAVER Jisik iN (knowledge exchange service between NAVER users) and to identify topics that capture public interest. Methods In this study, based on data collected on NAVER Jisik iN from July 2019 to July 2024 with the keyword ‘vasectomy,’ this paper performs a co-occurrence matrix analysis on the Q&A data to identify key terms related to the topic. By applying connection centrality and community detection techniques, this paper visualizes and examines the network graph to uncover relationships between words and identify the primary topics of discussion. Results The co-occurrence matrix analysis reveals that keywords such as ‘surgery,’ ‘vasectomy,’ and ‘contraception’ play a central role in discussions about vasectomies. Both the Q&A data highlight the vasectomy procedure and contraceptive effectiveness as primary topics of discussion. The visualization graphs intuitively display the centrality and strength of connections between these keywords, as well as the interrelatedness of topics through distinct community groups. Conclusions The analysis shows that the questions primarily focused on the surgical procedure, while the answers were more concerned with contraceptive effectiveness and physiological aspects. These results suggest that the public’s interest in vasectomy is closely linked to its effectiveness, potential side effects, and related social issues.

Facial paralysis refers to the abnormal behavior of facial muscles caused by a disorder of the facial nerve, mainly manifested as facial asymmetry. In recent years, deep learning has found extensive applications in facial paralysis detection research. However, most existing methods are constrained to assessing the severity of facial paralysis, thereby concealing crucial symptoms within black-box models. Compared to the severity of facial paralysis, the symptoms of facial paralysis are of greater significance to both physicians and patients. To address this issue, this paper proposes a facial paralysis symptom detection model based on facial action units (AUs). To enhance the accuracy of AU intensity prediction, a novel Difference Ensemble Method (DEM) is introduced. This method leverages differential information between frames within the same video to improve the accuracy of predictions for the current frame. Building upon the predicted AU intensity sequences for keyframes in a video, an interpretable model for detecting facial paralysis symptoms is designed. This model employs an active means to describe the asymmetry in facial muscle strength and utilizes co-occurrence matrices to detect synkinesis. It is noteworthy that DEM is exclusively trained on a dataset of normal faces but exhibits excellent performance when transferred to a facial paralysis dataset. Additionally, DEM exhibits higher accuracy in predicting AU intensity compared to existing methods. The F1 scores for detecting facial muscle function in the eyebrow, eye, and mouth regions with our proposed model are 80.0%, 79.23%, and 90.91%, respectively. To demonstrate the model’s performance, a synkinesis detection experiment is conducted, further validating its applicability in facial paralysis detection.

Purpose: The purpose of this paper is to explore public perceptions and key issues related to vasectomy in South Korea. In particular, this paper seeks to analyze how vasectomy is discussed on question-and-answer (Q&A) social platforms including NAVER Jisik iN (knowledge exchange service between NAVER users) and to identify topics that capture public interest., Methods: In this study, based on data collected on NAVER Jisik iN from July 2019 to July 2024 with the keyword 'vasectomy,' this paper performs a co-occurrence matrix analysis on the Q&A data to identify key terms related to the topic. By applying connection centrality and community detection techniques, this paper visualizes and examines the network graph to uncover relationships between words and identify the primary topics of discussion., Results: The co-occurrence matrix analysis reveals that keywords such as 'surgery,' 'vasectomy,' and 'contraception' play a central role in discussions about vasectomies. Both the Q&A data highlight the vasectomy procedure and contraceptive effectiveness as primary topics of discussion. The visualization graphs intuitively display the centrality and strength of connections between these keywords, as well as the interrelatedness of topics through distinct community groups., Conclusion: The analysis shows that the questions primarily focused on the surgical procedure, while the answers were more concerned with contraceptive effectiveness and physiological aspects. These results suggest that the public's interest in vasectomy is closely linked to its effectiveness, potential side effects, and related social issues.

Ductile fracture occurs with large plastic deformation prior to fracture and is primarily characterized by voids of varying sizes. In this work, a new approach based on machine learning is proposed for the characterization of ductile fractures. The method is based on the application of a pre-processing of the fractographies, followed by a processing that includes the generation of a mask through a mathematical morphology, a classification is made according to their size in micrometers to later make a histogram of the size of the cavity in relation respect to its frequency of repetition in each fractography. Besides, the histogram is then normalized to generate a Probability Density Function (PDF). Based on the PDF, a third-order statistical metric known as Fractography Skewness (FS) is used to evaluate fracture susceptibility and is related to the Degree of Fracture Susceptibility (DFS). If the FS is negative (indicating a left-skewed PDF), the fractography is characterized by large voids and is assigned a DFS of 1, indicating low susceptibility. Conversely, a positive FS (indicating a right-skewed PDF) indicates the presence of small voids, resulting in a DFS of 3, indicating high susceptibility. The proposed quantitative methodology demonstrates a higher accuracy than the most advanced current techniques based on deep learning and the subjectivity of the specialist. • A novel ductile fracture evaluation framework has been described. • New concepts: Fractography Skewness and the Degree of Fracture Susceptibility. • Machine learning based method for classifying ductile fractures. • Higher accuracy than current techniques based on the subjectivity of the specialist. [ABSTRACT FROM AUTHOR]

[Display omitted] • Detailed lithological and structural mapping using textural analysis. • The approach allows for the depiction of structural features and their reverberations within the host rocks. • Pan-sharpening with PRISM data (2.5 m) greatly assists in constructing a primitive microstructural pattern. Detailed geological mapping is the decisive key for mineral deposit prospecting, deciphering tectonic models, and outlining the main framework for most development programs and constructions. Without a doubt, various remote sensing datasets have introduced reliable lithological and structural mapping solutions. The main defect with remote sensing data is the curse of dimensionality, especially with hyperspectral data, where a lot of time is spent handling and selecting representative bands for various geological analyses. Consequently, and for the first time, our research is an attempt to resolve the complicated structural patterns (lineaments, folds, foliations, and cross-cutting relationships) and enhance lithological discrimination using a single band (of Sentinel 2 and ALOS PRISM data) and textural analysis. Through several trials over different pixel sizes (2.5 m and 10 m) and various kernels (3 × 3, 7 × 7, or 11 × 11), reasonable results are obtained, enabling lithological discrimination, in-depth structural analysis (foliations, faults, joints, and folds), shape recognition of systematic rock bodies, and delineation of quaternary deposits using single band analysis rather than time-consuming multiple band processing. Our results have been verified using intensive fieldwork and accurate visual interpretations using different datasets (e.g., previous geological maps, remote sensing data, etc.). Upon field verification and petrographical investigations of this research outcomes, we strongly recommend the adopted approach for the geological community, as it opens the doors for various applications utilizing single-band second-order statistics. We expect that this research could significantly help the geological community by reviving several previous studies and being applicable for future research, besides offering a reasonable approach for minimizing the time and efforts required for detailed field studies by highlighting micro- and mesoscale structures. [ABSTRACT FROM AUTHOR]

Background: Despite knowledge of qualitative changes that occur on ultrasound in tendinopathy, there is currently no objective and reliable means to quantify the severity or prognosis of tendinopathy on ultrasound., Objective: The primary objective of this study is to produce a quantitative and automated means of inferring potential structural changes in tendinopathy by developing and implementing an algorithm which performs a texture based segmentation of tendon ultrasound (US) images., Method: A model-based segmentation approach is used which combines Gaussian mixture models, Markov random field theory and grey-level co-occurrence (GLCM) features. The algorithm is trained and tested on 49 longitudinal B-mode ultrasound images of the Achilles tendons which are labelled as tendinopathic (24) or healthy (25). Hyperparameters are tuned, using a training set of 25 images, to optimise a decision tree based classification of the images from texture class proportions. We segment and classify the remaining test images using the decision tree., Results: Our approach successfully detects a difference in the texture profiles of tendinopathic and healthy tendons, with 22/24 of the test images accurately classified based on a simple texture proportion cut-off threshold. Results for the tendinopathic images are also collated to gain insight into the topology of structural changes that occur with tendinopathy. It is evident that distinct textures, which are predominantly present in tendinopathic tendons, appear most commonly near the transverse boundary of the tendon, though there was a large variability among diseased tendons., Conclusion: The GLCM based segmentation of tendons under ultrasound resulted in distinct segmentations between healthy and tendinopathic tendons and provides a potential tool to objectively quantify damage in tendinopathy., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)